Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

  • Bouwmans T
  • Baf F
  • Vachon B
  • 23


    Mendeley users who have this article in their library.
  • N/A


    Citations of this article.


Mixture of Gaussians is a widely used approach for background modeling to detect moving objects from static cameras. Numerous improvements of the original method developed by Stauffer and Grimson [1] have been proposed over the recent years and the purpose of this paper is to provide a survey and an original classification of these improvements. We also discuss relevant issues to reduce the computation time. Firstly, the original MOG are reminded and discussed following the challenges met in video sequences. Then, we categorize the different improvements found in the literature. We have classified them in term of strategies used to improve the original MOG and we have discussed them in term of the critical situations they claim to handle. After analyzing the strategies and identifying their limitations, we conclude with several promising directions for future research.

Author-supplied keywords

  • Background Modeling
  • Foreground Detection
  • Mixture of Gaussians

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

There are no full text links


  • T Bouwmans

  • F El Baf

  • B Vachon

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free